

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest



class 						_A     (				snake_case				):





  '''simple docstring'''



  __lowerCamelCase			:      Optional[Any]					=   (PNDMScheduler,)
  __lowerCamelCase			:      Dict					=   (('''num_inference_steps''', 5_0),)





  def    snake_case_       (  self   ,**SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		List[str]        =		{
          """num_train_timesteps""": 1000,
          """beta_start""": 0.00_01,
          """beta_end""": 0.02,
          """beta_schedule""": """linear""",
      }

      config.update(**SCREAMING_SNAKE_CASE_    )
      return config





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_=0   ,**SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		List[Any]        =		dict(self.forward_default_kwargs    )
      snake_case							:		List[str]        =		kwargs.pop("""num_inference_steps"""   ,SCREAMING_SNAKE_CASE_    )
      snake_case							:		str        =		self.dummy_sample
      snake_case							:		Dict        =		0.1 * sample
      snake_case							:		str        =		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

      for scheduler_class in self.scheduler_classes:
          snake_case							:		Union[str, Any]        =		self.get_scheduler_config(**SCREAMING_SNAKE_CASE_    )
          snake_case							:		Dict        =		scheduler_class(**SCREAMING_SNAKE_CASE_    )
          scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    )
          # copy over dummy past residuals
          snake_case							:		str        =		dummy_past_residuals[:]

          with tempfile.TemporaryDirectory() as tmpdirname:
              scheduler.save_config(SCREAMING_SNAKE_CASE_    )
              snake_case							:		int        =		scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE_    )
              new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    )
              # copy over dummy past residuals
              snake_case							:		List[str]        =		dummy_past_residuals[:]

          snake_case							:		int        =		scheduler.step_prk(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    ).prev_sample
          snake_case							:		Tuple        =		new_scheduler.step_prk(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    ).prev_sample

          assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"

          snake_case							:		Union[str, Any]        =		scheduler.step_plms(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    ).prev_sample
          snake_case							:		List[str]        =		new_scheduler.step_plms(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    ).prev_sample

          assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"





  def    snake_case_       (  self    ):

      '''simple docstring'''
      pass





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_=0   ,**SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		List[str]        =		dict(self.forward_default_kwargs    )
      snake_case							:		Union[str, Any]        =		kwargs.pop("""num_inference_steps"""   ,SCREAMING_SNAKE_CASE_    )
      snake_case							:		Dict        =		self.dummy_sample
      snake_case							:		Any        =		0.1 * sample
      snake_case							:		List[Any]        =		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

      for scheduler_class in self.scheduler_classes:
          snake_case							:		Union[str, Any]        =		self.get_scheduler_config()
          snake_case							:		Optional[int]        =		scheduler_class(**SCREAMING_SNAKE_CASE_    )
          scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    )

          # copy over dummy past residuals (must be after setting timesteps)
          snake_case							:		str        =		dummy_past_residuals[:]

          with tempfile.TemporaryDirectory() as tmpdirname:
              scheduler.save_config(SCREAMING_SNAKE_CASE_    )
              snake_case							:		Dict        =		scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE_    )
              # copy over dummy past residuals
              new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    )

              # copy over dummy past residual (must be after setting timesteps)
              snake_case							:		Dict        =		dummy_past_residuals[:]

          snake_case							:		List[str]        =		scheduler.step_prk(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    ).prev_sample
          snake_case							:		Optional[int]        =		new_scheduler.step_prk(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    ).prev_sample

          assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"

          snake_case							:		List[str]        =		scheduler.step_plms(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    ).prev_sample
          snake_case							:		Optional[Any]        =		new_scheduler.step_plms(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    ).prev_sample

          assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"





  def    snake_case_       (  self   ,**SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Optional[Any]        =		self.scheduler_classes[0]
      snake_case							:		Any        =		self.get_scheduler_config(**SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[int]        =		scheduler_class(**SCREAMING_SNAKE_CASE_    )

      snake_case							:		Any        =		10
      snake_case							:		Union[str, Any]        =		self.dummy_model()
      snake_case							:		Optional[Any]        =		self.dummy_sample_deter
      scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    )

      for i, t in enumerate(scheduler.prk_timesteps    ):
          snake_case							:		List[Any]        =		model(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )
          snake_case							:		Optional[int]        =		scheduler.step_prk(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ).prev_sample

      for i, t in enumerate(scheduler.plms_timesteps    ):
          snake_case							:		List[Any]        =		model(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )
          snake_case							:		List[str]        =		scheduler.step_plms(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ).prev_sample

      return sample





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Tuple        =		dict(self.forward_default_kwargs    )

      snake_case							:		Union[str, Any]        =		kwargs.pop("""num_inference_steps"""   ,SCREAMING_SNAKE_CASE_    )

      for scheduler_class in self.scheduler_classes:
          snake_case							:		Dict        =		self.get_scheduler_config()
          snake_case							:		Optional[Any]        =		scheduler_class(**SCREAMING_SNAKE_CASE_    )

          snake_case							:		str        =		self.dummy_sample
          snake_case							:		str        =		0.1 * sample

          if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE_   ,"""set_timesteps"""    ):
              scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    )
          elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE_   ,"""set_timesteps"""    ):
              snake_case							:		Tuple        =		num_inference_steps

          # copy over dummy past residuals (must be done after set_timesteps)
          snake_case							:		Any        =		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
          snake_case							:		Optional[Any]        =		dummy_past_residuals[:]

          snake_case							:		List[str]        =		scheduler.step_prk(SCREAMING_SNAKE_CASE_   ,0   ,SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    ).prev_sample
          snake_case							:		List[str]        =		scheduler.step_prk(SCREAMING_SNAKE_CASE_   ,1   ,SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    ).prev_sample

          self.assertEqual(output_a.shape   ,sample.shape    )
          self.assertEqual(output_a.shape   ,output_a.shape    )

          snake_case							:		List[Any]        =		scheduler.step_plms(SCREAMING_SNAKE_CASE_   ,0   ,SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    ).prev_sample
          snake_case							:		List[Any]        =		scheduler.step_plms(SCREAMING_SNAKE_CASE_   ,1   ,SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    ).prev_sample

          self.assertEqual(output_a.shape   ,sample.shape    )
          self.assertEqual(output_a.shape   ,output_a.shape    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      for timesteps in [100, 1000]:
          self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      for steps_offset in [0, 1]:
          self.check_over_configs(steps_offset=SCREAMING_SNAKE_CASE_    )

      snake_case							:		Dict        =		self.scheduler_classes[0]
      snake_case							:		str        =		self.get_scheduler_config(steps_offset=1    )
      snake_case							:		Optional[Any]        =		scheduler_class(**SCREAMING_SNAKE_CASE_    )
      scheduler.set_timesteps(10    )
      assert torch.equal(
          scheduler.timesteps   ,torch.LongTensor(
              [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]    )   ,)





  def    snake_case_       (  self    ):

      '''simple docstring'''
      for beta_start, beta_end in zip([0.00_01, 0.0_01]   ,[0.0_02, 0.02]    ):
          self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_   ,beta_end=SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      for t in [1, 5, 10]:
          self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      for t, num_inference_steps in zip([1, 5, 10]   ,[10, 50, 100]    ):
          self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
      snake_case							:		str        =		27

      for scheduler_class in self.scheduler_classes:
          snake_case							:		Optional[int]        =		self.dummy_sample
          snake_case							:		Optional[int]        =		0.1 * sample

          snake_case							:		Dict        =		self.get_scheduler_config()
          snake_case							:		int        =		scheduler_class(**SCREAMING_SNAKE_CASE_    )

          scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    )

          # before power of 3 fix, would error on first step, so we only need to do two
          for i, t in enumerate(scheduler.prk_timesteps[:2]    ):
              snake_case							:		Union[str, Any]        =		scheduler.step_prk(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ).prev_sample





  def    snake_case_       (  self    ):

      '''simple docstring'''
      with self.assertRaises(SCREAMING_SNAKE_CASE_    ):
          snake_case							:		Union[str, Any]        =		self.scheduler_classes[0]
          snake_case							:		Union[str, Any]        =		self.get_scheduler_config()
          snake_case							:		Optional[int]        =		scheduler_class(**SCREAMING_SNAKE_CASE_    )

          scheduler.step_plms(self.dummy_sample   ,1   ,self.dummy_sample    ).prev_sample





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		List[Any]        =		self.full_loop()
      snake_case							:		List[str]        =		torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
      snake_case							:		int        =		torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

      assert abs(result_sum.item() - 1_98.13_18    ) < 1E-2
      assert abs(result_mean.item() - 0.25_80    ) < 1E-3





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		List[str]        =		self.full_loop(prediction_type="""v_prediction"""    )
      snake_case							:		Optional[Any]        =		torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
      snake_case							:		Optional[Any]        =		torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

      assert abs(result_sum.item() - 67.39_86    ) < 1E-2
      assert abs(result_mean.item() - 0.08_78    ) < 1E-3





  def    snake_case_       (  self    ):

      '''simple docstring'''
      # We specify different beta, so that the first alpha is 0.99
      snake_case							:		Any        =		self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_   ,beta_start=0.01    )
      snake_case							:		List[str]        =		torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
      snake_case							:		Optional[int]        =		torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

      assert abs(result_sum.item() - 2_30.03_99    ) < 1E-2
      assert abs(result_mean.item() - 0.29_95    ) < 1E-3





  def    snake_case_       (  self    ):

      '''simple docstring'''
      # We specify different beta, so that the first alpha is 0.99
      snake_case							:		List[str]        =		self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_   ,beta_start=0.01    )
      snake_case							:		Any        =		torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
      snake_case							:		Dict        =		torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

      assert abs(result_sum.item() - 1_86.94_82    ) < 1E-2
      assert abs(result_mean.item() - 0.24_34    ) < 1E-3









'''simple docstring'''


# Usage:
# ./gen-card-facebook-wmt19.py

import os
from pathlib import Path


def     SCREAMING_SNAKE_CASE							(   a_						:				str					,       a_						:				Union[str, Any]					,       a_						:				Dict      ):
  __a								=       {
      'en': 'Machine learning is great, isn\'t it?',
      'ru': 'Машинное обучение - это здорово, не так ли?',
      'de': 'Maschinelles Lernen ist großartig, oder?',
  }

  # BLUE scores as follows:
  # "pair": [fairseq, transformers]
  __a								=       {
      'ru-en': ['[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)', '39.20'],
      'en-ru': ['[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)', '33.47'],
      'en-de': ['[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)', '42.83'],
      'de-en': ['[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)', '41.35'],
  }
  __a								=       f"{src_lang}-{tgt_lang}"

  __a								=       f"\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"facebook/wmt19-{src_lang}-{tgt_lang}\"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = \"{texts[src_lang]}\"\ninput_ids = tokenizer.encode(input, return_tensors=\"pt\")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n- The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair   | fairseq | transformers\n-------|---------|----------\n{pair}  | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=15\nmkdir -p $DATA_DIR\nsacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS\n```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\n\n\n### BibTeX entry and citation info\n\n```bibtex\n@inproceedings{{...,\n  year={{2020}},\n  title={{Facebook FAIR's WMT19 News Translation Task Submission}},\n  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n  booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n"
  os.makedirs(a_					,       exist_ok=a_      )
  __a								=       os.path.join(a_					,       'README.md'      )
  print(f"Generating {path}"      )
  with open(a_					,       'w'					,       encoding='utf-8'      ) as f:
    f.write(a_      )


# make sure we are under the root of the project
UpperCAmelCase_					=   Path(__file__).resolve().parent.parent.parent
UpperCAmelCase_					=   repo_dir / "model_cards"

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
  UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_					=   model_name.split("-")
  UpperCAmelCase_					=   model_cards_dir / "facebook" / model_name
  write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)



